GeorgeSeif

# coding: utf-8

import spacy
import textacy.extract

### Load spaCy's English NLP model
nlp = spacy.load('en_core_web_lg')

### The text we want to examine
text = """Washington, D.C., formally the District of Columbia and commonly referred to as Washington or D.C., is the capital of the United States of America.[4] Founded after the American Revolution as the seat of government of the newly independent country, Washington was named after George Washington, first President of the United States and Founding Father.[5] Washington is the principal city of the Washington metropolitan area, which has a population of 6,131,977.[6] As the seat of the United States federal government and several international organizations, the city is an important world political capital.[7] Washington is one of the most visited cities in the world, with more than 20 million annual tourists.[8][9]

GeorgeSeif

GeorgeSeif

GeorgeSeif

# A string variable
name = "george"
statement = " is great"
### The variable "sentence" now contains the string "george is great"
sentence = name + statement 

# An integer variable
age = 24
factor = 2
### The variable "result" now contains the integer 48

GeorgeSeif

a = 2000

if a > 1000:
  print("Big number!")
elif a >= 500 and a < 1000:
  print("Medium number!")
elif a >= 0 and a < 500:
  print("Small number!")
else:

GeorgeSeif

# A for loop printing numbers from 1 to 10
for num in range(1, 11):
  print(num)
  
# A while loop printing numbers from 1 to 10
num = 1
while num < 11:
  print(num)
  num = num + 1
 

GeorgeSeif

# We can easily story any kind of data in a list or tuple
a = [1, 2, 3]
b = (4, 5, 6)

a = [1, 2.6, "hello"]
b = (4, 5.8, "goodbye")

# We can initialize an empty list and populate late it dynamically too
a = list() # Method 1 of initializing
a = [] # Method 2 of intializing (same results as method 1!)

GeorgeSeif

# Data is stored in a dictionary as key-values pairs
my_dict = { 
  "bob": "bob1991@gmail.com",
  "claire": "claire1986@gmail.com",
  "john": "john_22@gmail.com"
}

# You can initialize an empty dictionary in 2 ways
my_dict = {}
my_dict = dict()

GeorgeSeif

import math
from keras import backend as K

# Define our custom metric
def PSNR(y_true, y_pred):
    max_pixel = 1.0
    return 10.0 * math.log10((max_pixel ** 2) / (K.mean(K.square(y_pred - y_true)))) 

# Define our custom loss function
def charbonnier(y_true, y_pred):

GeorgeSeif

import tensorflow as tf

def tf_int_round(num):
	return tf.cast(tf.round(num), dtype=tf.int32)

class resize_layer(layers.Layer):
    # Initialize variables
    def __init__(self, scale, **kwargs):
        self.scale = scale
        super(resize_layer, self).__init__(**kwargs)